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Machine Learning (ML)

Machine Learning is the fastest growing and most potential field that enables a computer to perform specific tasks better than humans. It is actively used in companies like Apple, Tesla, Google and Facebook. We are covering the latest developments in the field

Machine Learning (ML)

Text Summarization using Transformers

In this article, we will learn about the fundamentals of Text Summarization, some of the different ways in which we can summarize text, Transformers, the BART model, and finally, we will practically implement some of these concepts.

Time Series Classification

In this article, we will learn about a beginner-level approach to time series classification.

Fuzzy Relations, Propositions, Implications and Inferences

In this article, we will first learn about fuzzy relations and the different types of operations that can be performed on them. Then, we will learn about the truth values of fuzzy propositions, about fuzzy implications or if-then rules.

Introduction to Feature Engineering

In this article, we will be learning about an important step in the machine learning process: feature engineering.

Machine Learning (ML)

Convolution Filters / Filters in CNN

Convolution filters are filters (multi-dimensional data) used in Convolution layer which helps in extracting specific features from input data. There are different types of Filters like Gaussian Blur, Prewitt Filter and many more which we have covered.

Machine Learning (ML)

Performance Comparison of Different Models and Data Preprocessing Techniques

In this article, we will be comparing the performance of different data preprocessing techniques (specifically, different ways of handling missing values and categorical variables) and machine learning models applied to a tabular dataset.

Machine Learning (ML)

SpineNet

SpineNet proposes an alternative to ResNet50, a variant of the ResNet model which uses 50 layers of deep convolutional network (hence "50" in its name). It intends to disrupt the CNN architecture from a high level which has not changed over the years.

Machine Learning (ML)

RefineDet model

RefineDet model is a popular Deep Learning model that is used for Object Detection applications as an alternative to SSD and YOLO based CNN models.

Machine Learning (ML)

Flattened Convolutional Neural Network

In this article, we have explored the idea of Flattened Convolutional Neural Network and the problem of conventional CNN it solves.

Machine Learning (ML)

RefineNet Model

In this article, we have explained RefineNet Model in depth which is a deep learning model used for Semantic Segmentation.

Machine Learning (ML)

Jensen Shannon Divergence

Jensen Shannon Divergence is one of the distribution comparison techniques that can be easily used in parametric tests in ML.

Machine Learning (ML)

Person re-identification ReID

In this article, we have explored the idea behind Person re-identification ReID applications, techniques for ReID and real world applications.

Machine Learning (ML)

Commonly Used Neural Networks

We have explored the commonly used Neural Networks like Hebbian Neural Networks, Auto-Associative Neural Networks, Hopfield Neural Networks, Radial Basis Function Neural Networks and much more.

Machine Learning (ML)

Token Classification in Python with HuggingFace

In this article, we will learn about token classification, its applications, and how it can be implemented in Python using the HuggingFace library.

Machine Learning (ML)

Different Hyperparameter optimization techniques

In this article, we will explore various techniques used for optimizing hyperparameters of the machine learning model such as Grid Search, Bayesian Optimization, Halving randomized search and much more.

clustering algorithm

Spectral Clustering

Spectral clustering is an interesting Unsupervised clustering algorithm that is capable of correctly clustering Non-convex data by the use of clever Linear algebra.

Machine Learning (ML)

Conditional Generative Adversarial Nets

In this article, we have explained the concept of Conditional Generative Adversarial Nets in depth.

What is Neural Network and Deep Learning?

Deep Learning has become quite a buzzword in recent years. It has taken over in all applications from tasks like image recognition, chatbots like Alexa and Google Assistant to defeating world champions in a complex games like Go and Dota 2.

Machine Learning (ML)

Different Classification Trees in Machine Learning

In this article, we will be learning about different Classification tree methods such as C4.5, CHAID and much more along with some key similarities and differences between them. An implementation in Python is also explained.

Machine Learning (ML)

Local response normalization (LRN)

In this article, we have explained the concept of Local response normalization (LRN) in depth along with comparison with Batch Normalization.

Neural Architecture Search (NAS)

The aim of this article is to provide a clear and intuitive understanding of the deep learning paradigm known as Neural Architecture Search (NAS).

Time Series Analysis/ Forecasting Techniques + Models

In this article, we will understand why time series analysis is important and how it is done using different techniques like Spectral analysis and different time series models like Auto-regressive (AR) model.

Machine Learning (ML)

Applications of GANs

In this article, we have explored the different applications of GANs such as Image Inpainting, Steganography and much more. A Generative Adversarial Network, or GAN, is a generative modeling neural network architecture.

Introduction to Time Series Data

In this article, we will get an understanding of Time Series Data along with different types of time series, analysis and forecasting.

Machine Learning (ML)

Denoising Autoencoders (DAEs)

The aim of this article is to discuss the Denoising Autoencoder (DAE) in sufficient detail. Hopefully, by the end of the article, readers would have obtained an understanding of the denoising autoencoder.